Analysis of GLDS-205 from NASA GeneLab

This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven

Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491

1. Read data

First we set up the working directory to where the files are saved.

 setwd('~/Documents/HTML_R/GLDS205')

R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.

 if(file.exists('iDEP_core_functions.R'))
    source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).

 inputFile <- 'GLDS205_Expression.csv'
 sampleInfoFile <- 'GLDS205_Sampleinfo.csv'
 gldsMetadataFile <- 'GLDS205_Metadata.csv'
 geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
 geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db'  # pathway database in SQL; can be GMT format 
 STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

 input_missingValue <- 'geneMedian' #Missing values imputation method
 input_dataFileFormat <- 1  #1- read counts, 2 FKPM/RPKM or DNA microarray
 input_minCounts <- 0.5 #Min counts
 input_NminSamples <- 1 #Minimum number of samples 
 input_countsLogStart <- 4  #Pseudo count for log CPM
 input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr)   #  install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
WT_FLT_Rep1 WT_FLT_Rep2 WT_FLT_Rep3 WT_FLT_Rep4 WT_FLT_Rep5 WT_GC_Rep1 WT_GC_Rep2 WT_GC_Rep3 WT_GC_Rep4 WT_GC_Rep5 HSFA2KO_FLT_Rep1 HSFA2KO_FLT_Rep2 HSFA2KO_FLT_Rep3 HSFA2KO_GC_Rep1 HSFA2KO_GC_Rep2 HSFA2KO_GC_Rep3
Sample.LongId Atha.Col.0.HypocotylCC.WT.FLT.Rep1.Array Atha.Col.0.HypocotylCC.WT.FLT.Rep2.Array Atha.Col.0.HypocotylCC.WT.FLT.Rep3.Array Atha.Col.0.HypocotylCC.WT.FLT.Rep4.Array Atha.Col.0.HypocotylCC.WT.FLT.Rep5.Array Atha.Col.0.HypocotylCC.WT.GC.Rep1.Array Atha.Col.0.HypocotylCC.WT.GC.Rep2.Array Atha.Col.0.HypocotylCC.WT.GC.Rep3.Array Atha.Col.0.HypocotylCC.WT.GC.Rep4.Array Atha.Col.0.HypocotylCC.WT.GC.Rep5.Array Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep1.Array Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep2.Array Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep3.Array Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep1.Array Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep2.Array Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep3.Array
Sample.Id Atha.Col.0.HypocotylCC.WT.FLT.Rep1 Atha.Col.0.HypocotylCC.WT.FLT.Rep2 Atha.Col.0.HypocotylCC.WT.FLT.Rep3 Atha.Col.0.HypocotylCC.WT.FLT.Rep4 Atha.Col.0.HypocotylCC.WT.FLT.Rep5 Atha.Col.0.HypocotylCC.WT.GC.Rep1 Atha.Col.0.HypocotylCC.WT.GC.Rep2 Atha.Col.0.HypocotylCC.WT.GC.Rep3 Atha.Col.0.HypocotylCC.WT.GC.Rep4 Atha.Col.0.HypocotylCC.WT.GC.Rep5 Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep1 Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep2 Atha.Col.0.HypocotylCC.HSFA2.KO.FLT.Rep3 Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep1 Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep2 Atha.Col.0.HypocotylCC.HSFA2.KO.GC.Rep3
Sample.Name Atha_Col-0_HypocotylCC_WT_FLT_Rep1 Atha_Col-0_HypocotylCC_WT_FLT_Rep2 Atha_Col-0_HypocotylCC_WT_FLT_Rep3 Atha_Col-0_HypocotylCC_WT_FLT_Rep4 Atha_Col-0_HypocotylCC_WT_FLT_Rep5 Atha_Col-0_HypocotylCC_WT_GC_Rep1 Atha_Col-0_HypocotylCC_WT_GC_Rep2 Atha_Col-0_HypocotylCC_WT_GC_Rep3 Atha_Col-0_HypocotylCC_WT_GC_Rep4 Atha_Col-0_HypocotylCC_WT_GC_Rep5 Atha_Col-0_HypocotylCC_HSFA2-KO_FLT_Rep1 Atha_Col-0_HypocotylCC_HSFA2-KO_FLT_Rep2 Atha_Col-0_HypocotylCC_HSFA2-KO_FLT_Rep3 Atha_Col-0_HypocotylCC_HSFA2-KO_GC_Rep1 Atha_Col-0_HypocotylCC_HSFA2-KO_GC_Rep2 Atha_Col-0_HypocotylCC_HSFA2-KO_GC_Rep3
GLDS 205 205 205 205 205 205 205 205 205 205 205 205 205 205 205 205
Accession GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205 GLDS-205
Hardware BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC BRIC
Tissue Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures
Age 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days 14 days
Organism Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana
Ecotype Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0 Col-0
Genotype WT WT WT WT WT WT WT WT WT WT hsfA2 hsfA2 hsfA2 hsfA2 hsfA2 hsfA2
Variety Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 hsfA2 Col-0 hsfA2 Col-0 hsfA2 Col-0 hsfA2 Col-0 hsfA2 Col-0 hsfA2
Radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth Background Earth Background Earth Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth Background Earth
Gravity Microgravity Microgravity Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Microgravity Microgravity Microgravity Terrestrial Terrestrial Terrestrial
Developmental 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture 14 day old cell culture
Time.series.or.Concentration.gradient Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point Single time point
Light Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark Dark
Assay..RNAseq. Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling
Temperature 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24 22-24
Treatment.type HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment HSFA2 functions in the physiological adaptation of undifferentiated plant cells to spaceflight microgravity environment
Treatment.intensity x x x x x x x x x x x x x x x x
Treament.timing x x x x x x x x x x x x x x x x
Preservation.Method. RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater 
 readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
   kable( head(readData.out$data) ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
WT_FLT_Rep1 WT_FLT_Rep2 WT_FLT_Rep3 WT_FLT_Rep4 WT_FLT_Rep5 WT_GC_Rep1 WT_GC_Rep2 WT_GC_Rep3 WT_GC_Rep4 WT_GC_Rep5 HSFA2KO_FLT_Rep1 HSFA2KO_FLT_Rep2 HSFA2KO_FLT_Rep3 HSFA2KO_GC_Rep1 HSFA2KO_GC_Rep2 HSFA2KO_GC_Rep3
AT2G26150 3.700440 3.700440 3.700440 3.700440 3.700440 3.584963 3.459432 3.584963 3.700440 3.584963 2.807355 2.807355 2.807355 2.584963 2.584963 2.807355
ATCG01040 3.584963 3.584963 3.459432 3.169925 3.584963 3.459432 3.584963 3.459432 3.459432 3.321928 3.459432 3.700440 3.169925 3.584963 3.459432 3.000000
ATCG00440 3.459432 3.321928 3.169925 3.000000 3.459432 3.321928 3.321928 3.321928 3.169925 3.169925 3.169925 3.584963 3.000000 3.459432 3.169925 2.807355
AT3G62100 3.000000 3.000000 3.000000 3.000000 2.584963 2.807355 3.000000 2.807355 2.807355 3.000000 3.321928 3.000000 3.169925 3.321928 3.459432 3.169925
AT1G30700 2.807355 2.807355 3.000000 3.321928 2.584963 3.000000 2.807355 2.807355 2.807355 3.000000 3.321928 3.169925 3.321928 3.169925 3.321928 3.000000
AT3G20340 3.321928 3.459432 3.459432 3.584963 3.700440 3.459432 3.584963 3.459432 3.584963 3.700440 3.321928 3.321928 3.169925 3.169925 3.321928 3.000000 
 readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
 kable( readSampleInfo.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Gravity Variety
WT_FLT_Rep1 Microgravity Col0 WT
WT_FLT_Rep2 Microgravity Col0 WT
WT_FLT_Rep3 Microgravity Col0 WT
WT_FLT_Rep4 Microgravity Col0 WT
WT_FLT_Rep5 Microgravity Col0 WT
WT_GC_Rep1 Terrestrial Col0 WT
WT_GC_Rep2 Terrestrial Col0 WT
WT_GC_Rep3 Terrestrial Col0 WT
WT_GC_Rep4 Terrestrial Col0 WT
WT_GC_Rep5 Terrestrial Col0 WT
HSFA2KO_FLT_Rep1 Microgravity Col0 hsfA2
HSFA2KO_FLT_Rep2 Microgravity Col0 hsfA2
HSFA2KO_FLT_Rep3 Microgravity Col0 hsfA2
HSFA2KO_GC_Rep1 Terrestrial Col0 hsfA2
HSFA2KO_GC_Rep2 Terrestrial Col0 hsfA2
HSFA2KO_GC_Rep3 Terrestrial Col0 hsfA2 
 input_selectOrg ="NEW" 
 input_selectGO <- 'GOBP'   #Gene set category 
 input_noIDConversion = TRUE  
 allGeneInfo.out <- geneInfo(geneInfoFile) 
 converted.out = NULL 
 convertedData.out <- convertedData()    
 nGenesFilter()  
## [1] "16156 genes in 16 samples. 16156  genes passed filter.\n Original gene IDs used."
 convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
 parDefault = par() 
 par(mar=c(12,4,2,2)) 
 # barplot of total read counts
 x <- readData.out$rawCounts
 groups = as.factor( detectGroups(colnames(x ) ) )
 if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
  col1 = rainbow(nlevels(groups))[ groups ]             
         
 barplot( colSums(x)/1e6, 
        col=col1,las=3, main="Total read counts (millions)")  

 readCountsBias()  # detecting bias in sequencing depth 
## [1] 0.8646095
## [1] 0.9024904
## [1] 0.4081283
## [1] "No bias detected"
 # Box plot 
 x = readData.out$data 
 boxplot(x, las = 2, col=col1,
    ylab='Transformed expression levels',
    main='Distribution of transformed data') 

 #Density plot 
 par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
 densityPlot()       

 # Scatter plot of the first two samples 
 plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
    main='Scatter plot of first two samples') 

 ####plot gene or gene family
 input_selectOrg ="BestMatch" 
 input_geneSearch <- 'HOXA' #Gene ID for searching 
 genePlot()  
## NULL
 input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar? 
 geneBarPlotError()       
## NULL

3. Heatmap

 # hierarchical clustering tree
 x <- readData.out$data
 maxGene <- apply(x,1,max)
 # remove bottom 25% lowly expressed genes, which inflate the PPC
 x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
 plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

 #Correlation matrix
 input_labelPCC <- TRUE #Show correlation coefficient? 
 correlationMatrix() 

 # Parameters for heatmap
 input_nGenes <- 500    #Top genes for heatmap
 input_geneCentering <- TRUE    #centering genes ?
 input_sampleCentering <- FALSE #Center by sample?
 input_geneNormalize <- FALSE   #Normalize by gene?
 input_sampleNormalize <- FALSE #Normalize by sample?
 input_noSampleClustering <- FALSE  #Use original sample order
 input_heatmapCutoff <- 4   #Remove outliers beyond number of SDs 
 input_distFunctions <- 1   #which distant funciton to use
 input_hclustFunctions <- 1 #Linkage type
 input_heatColors1 <- 1 #Colors
 input_selectFactorsHeatmap <- 'Gravity'    #Sample coloring factors 
 png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
 staticHeatmap() 
 dev.off()  
## png 
##   2

[heatmap] (heatmap.png)

 heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

 input_nGenesKNN <- 2000    #Number of genes fro k-Means
 input_nClusters <- 4   #Number of clusters 
 maxGeneClustering = 12000
 input_kmeansNormalization <- 'geneMean'    #Normalization
 input_KmeansReRun <- 0 #Random seed 

 distributionSD()  #Distribution of standard deviations 

 KmeansNclusters()  #Number of clusters 

 Kmeans.out = Kmeans()   #Running K-means 
 KmeansHeatmap()   #Heatmap for k-Means 

 #Read gene sets for enrichment analysis 
 sqlite  <- dbDriver('SQLite')
 input_selectGO3 <- 'GOBP'  #Gene set category
 input_minSetSize <- 15 #Min gene set size
 input_maxSetSize <- 2000   #Max gene set size 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO3,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 # Alternatively, users can use their own GMT files by
 #GeneSets.out <- readGMTRobust('somefile.GMT')  
 results <- KmeansGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 2.02e-25 119 Response to abiotic stimulus
2.82e-17 99 Response to organic substance
8.02e-15 82 Response to oxygen-containing compound
3.39e-14 83 Response to endogenous stimulus
3.39e-14 82 Response to hormone
4.85e-14 67 Response to acid chemical
6.06e-14 77 Cellular response to chemical stimulus
8.14e-13 91 Regulation of nucleobase-containing compound metabolic process
8.14e-13 88 Regulation of RNA metabolic process
8.91e-13 56 Response to inorganic substance
B 9.16e-29 102 Response to abiotic stimulus
3.10e-25 76 Response to external stimulus
3.43e-23 61 Response to external biotic stimulus
3.43e-23 72 Multi-organism process
3.43e-23 61 Response to other organism
5.93e-23 61 Response to biotic stimulus
4.36e-22 87 Response to organic substance
6.62e-20 74 Response to oxygen-containing compound
9.72e-20 75 Response to hormone
2.39e-19 75 Response to endogenous stimulus
C 1.84e-15 80 Response to abiotic stimulus
4.50e-13 43 Response to lipid
4.50e-13 63 Response to oxygen-containing compound
6.68e-13 71 Response to organic substance
1.29e-12 63 Response to hormone
2.35e-12 63 Response to endogenous stimulus
1.46e-11 75 Multicellular organism development
1.88e-11 50 Response to acid chemical
9.89e-11 56 Cellular response to chemical stimulus
2.66e-10 28 Cellular response to lipid
D 1.57e-25 99 Response to abiotic stimulus
7.47e-23 74 Response to external stimulus
1.06e-20 87 Response to organic substance
3.89e-19 76 Response to hormone
8.57e-19 76 Response to endogenous stimulus
2.32e-18 71 Cellular response to chemical stimulus
2.12e-17 54 Response to external biotic stimulus
2.12e-17 54 Response to other organism
3.50e-17 54 Response to biotic stimulus
5.27e-17 79 Cell communication 
 input_seedTSNE <- 0    #Random seed for t-SNE
 input_colorGenes <- TRUE   #Color genes in t-SNE plot? 
 tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

 input_selectFactors <- 'Gravity'   #Factor coded by color
 input_selectFactors2 <- 'Variety'  #Factor coded by shape
 input_tsneSeed2 <- 0   #Random seed for t-SNE 
 #PCA, MDS and t-SNE plots
 PCAplot()  

 MDSplot() 

 tSNEplot()  

 #Read gene sets for pathway analysis using PGSEA on principal components 
 input_selectGO6 <- 'GOBP' 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO6,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

 cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Variety (p=8.88e-07).

6. DEG1

 input_CountsDEGMethod <- 3 #DESeq2= 3,limma-voom=2,limma-trend=1 
 input_limmaPval <- 0.1 #FDR cutoff
 input_limmaFC <- 2 #Fold-change cutoff
 input_selectModelComprions <- 'Gravity: Microgravity vs. Terrestrial'  #Selected comparisons
 input_selectFactorsModel <- 'Gravity'  #Selected comparisons
 input_selectInteractions <- NULL   #Selected comparisons
 input_selectBlockFactorsModel <- NULL  #Selected comparisons
 factorReferenceLevels.out <- c('Gravity:Terrestrial') 

 limma.out <- limma()
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
## geth, : Estimated rdf < 1.0; not estimating variance
 DEG.data.out <- DEG.data()
 limma.out$comparisons 
## [1] "Microgravity-Terrestrial"
 input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
 input_UpDownRegulated <- FALSE #Split up and down regulated genes 
 vennPlot() # Venn diagram 

  sigGeneStats() # number of DEGs as figure 

  sigGeneStatsTable() # number of DEGs as table 
##                                       Comparisons Up Down
## Microgravity-Terrestrial Microgravity-Terrestrial  0    0

7. DEG2

 input_selectContrast <- 'Microgravity-Terrestrial' #Selected comparisons 
 selectedHeatmap.data.out <- selectedHeatmap.data()
 selectedHeatmap()   # heatmap for DEGs in selected comparison
## Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'
 # Save gene lists and data into files
 write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
 write.csv(DEG.data(),'DEG.data.csv' )
 write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
 input_selectGO2 <- 'GOBP'  #Gene set category 
 geneListData.out <- geneListData()  
 volcanoPlot()  

  scatterPlot()  

  MAplot()  

  geneListGOTable.out <- geneListGOTable()  
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO2,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_removeRedudantSets <- TRUE   #Remove highly redundant gene sets? 
 results <- geneListGO()  #Enrichment analysis
## Error in if (dim(results1)[2] == 1) return(results1) else {: argument is of length zero
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 2.02e-25 119 Response to abiotic stimulus
2.82e-17 99 Response to organic substance
8.02e-15 82 Response to oxygen-containing compound
3.39e-14 83 Response to endogenous stimulus
3.39e-14 82 Response to hormone
4.85e-14 67 Response to acid chemical
6.06e-14 77 Cellular response to chemical stimulus
8.14e-13 91 Regulation of nucleobase-containing compound metabolic process
8.14e-13 88 Regulation of RNA metabolic process
8.91e-13 56 Response to inorganic substance
B 9.16e-29 102 Response to abiotic stimulus
3.10e-25 76 Response to external stimulus
3.43e-23 61 Response to external biotic stimulus
3.43e-23 72 Multi-organism process
3.43e-23 61 Response to other organism
5.93e-23 61 Response to biotic stimulus
4.36e-22 87 Response to organic substance
6.62e-20 74 Response to oxygen-containing compound
9.72e-20 75 Response to hormone
2.39e-19 75 Response to endogenous stimulus
C 1.84e-15 80 Response to abiotic stimulus
4.50e-13 43 Response to lipid
4.50e-13 63 Response to oxygen-containing compound
6.68e-13 71 Response to organic substance
1.29e-12 63 Response to hormone
2.35e-12 63 Response to endogenous stimulus
1.46e-11 75 Multicellular organism development
1.88e-11 50 Response to acid chemical
9.89e-11 56 Cellular response to chemical stimulus
2.66e-10 28 Cellular response to lipid
D 1.57e-25 99 Response to abiotic stimulus
7.47e-23 74 Response to external stimulus
1.06e-20 87 Response to organic substance
3.89e-19 76 Response to hormone
8.57e-19 76 Response to endogenous stimulus
2.32e-18 71 Cellular response to chemical stimulus
2.12e-17 54 Response to external biotic stimulus
2.12e-17 54 Response to other organism
3.50e-17 54 Response to biotic stimulus
5.27e-17 79 Cell communication

STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.

 STRING10_species = read.csv(STRING10_speciesFile)  
 ix = grep('Arabidopsis thaliana', STRING10_species$official_name ) 
 findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
 findTaxonomyID.out  
## [1] 3702

Enrichment analysis using STRING

  STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in names(x) <- value: 'names' attribute [2] must be the same length as the vector [1]
 input_STRINGdbGO <- 'Process'  #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro' 
 results <- stringDB_GO_enrichmentData()  # enrichment using STRING 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
x
NULL

PPI network retrieval and analysis

 input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis 
 stringDB_network1(1) #Show PPI network 
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found

Generating interactive PPI

 write(stringDB_network_link(), 'PPI_results.html') # write results to html file 
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
 browseURL('PPI_results.html') # open in browser

8. Pathway analysis

 input_selectContrast1 <- 'Microgravity-Terrestrial'    #select Comparison 
 #input_selectContrast1 = limma.out$comparisons[3] # manually set
 input_selectGO <- 'GOBP'   #Gene set category 
 #input_selectGO='custom' # if custom gmt file
 input_minSetSize <- 15 #Min size for gene set
 input_maxSetSize <- 2000   #Max size for gene set 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_pathwayPvalCutoff <- 0.2 #FDR cutoff
 input_nPathwayShow <- 30   #Top pathways to show
 input_absoluteFold <- FALSE    #Use absolute values of fold-change?
 input_GenePvalCutoff <- 1  #FDR to remove genes 

 input_pathwayMethod = 1  # 1  GAGE
 gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  
   
 results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters  
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
“No significant pathway found.” adj.Pval
No significant pathway found. NULL 
 pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 
## NULL
  enrichmentNetwork(pathwayListData.out )  
## Error in `[.data.frame`(x, , pvalue): undefined columns selected
  enrichmentNetworkPlotly(pathwayListData.out) 
## Error in `[.data.frame`(x, , pvalue): undefined columns selected
 input_pathwayMethod = 3  # 1  fgsea 
 fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (73.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
 results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GSEA analysis: Microgravity vs Terrestrial NES Genes adj.Pval
Down GPI anchor metabolic process -1.7607 26 1.5e-01
GPI anchor biosynthetic process -1.7607 26 1.5e-01
Protein targeting to membrane -1.7105 52 1.5e-01
Cortical cytoskeleton organization -1.6681 43 1.8e-01
Phosphatidylinositol biosynthetic process -1.6528 36 1.8e-01
Establishment of protein localization to membrane -1.6409 82 8.9e-02
Lipoprotein metabolic process -1.6366 52 1.8e-01
Protein lipidation -1.6225 51 1.9e-01
Lipoprotein biosynthetic process -1.6225 51 1.9e-01
Protein localization to membrane -1.5956 100 9.7e-02
Membrane lipid biosynthetic process -1.5294 84 1.8e-01
Membrane lipid metabolic process -1.5218 107 1.4e-01
Protein targeting -1.3961 203 1.8e-01
Microtubule-based process -1.3931 189 1.8e-01
Intracellular protein transport -1.3455 497 8.9e-02
Cellular protein localization -1.3445 592 7.4e-02
Cellular macromolecule localization -1.3385 620 7.4e-02
Translation -1.318 622 7.4e-02
Intracellular transport -1.3171 629 7.4e-02
Establishment of localization in cell -1.3168 673 7.4e-02
Vesicle-mediated transport -1.3061 408 1.8e-01
Peptide biosynthetic process -1.3038 625 8.9e-02
Amide biosynthetic process -1.3036 686 7.4e-02
Cellular localization -1.2866 812 7.4e-02
Protein localization -1.2842 800 7.4e-02
Cellular developmental process -1.2805 694 9.7e-02
Organonitrogen compound biosynthetic process -1.2776 1465 7.4e-02
Peptide transport -1.2742 737 7.4e-02
Up Photosynthetic electron transport chain 1.662 46 1.8e-01
Xylan metabolic process 1.6491 44 2.0e-01 
  pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
   PGSEAplot() # pathway analysis using PGSEA 
## 
## Computing P values using ANOVA

9. Chromosome

 input_selectContrast2 <- 'Microgravity-Terrestrial'    #select Comparison 
 #input_selectContrast2 = limma.out$comparisons[3] # manually set
 input_limmaPvalViz <- 0.1  #FDR to filter genes
 input_limmaFCViz <- 2  #FDR to filter genes 
 genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion

10. Biclustering

 input_nGenesBiclust <- 1000    #Top genes for biclustering
 input_biclustMethod <- 'BCCC()'    #Method: 'BCCC', 'QUBIC', 'runibic' ... 
 biclustering.out = biclustering()  # run analysis

 input_selectBicluster <- 1 #select a cluster 
 biclustHeatmap()   # heatmap for selected cluster 

 input_selectGO4 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO4,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.8e-60 216 Response to abiotic stimulus
4.0e-48 189 Response to organic substance
2.1e-46 168 Response to hormone
1.7e-45 168 Response to endogenous stimulus
5.0e-42 158 Response to oxygen-containing compound
2.9e-39 149 Cellular response to chemical stimulus
7.1e-35 133 Response to external stimulus
1.3e-32 160 Cell communication
1.3e-32 106 Response to inorganic substance
2.4e-32 120 Response to acid chemical

11. Co-expression network

 input_mySoftPower <- 5 #SoftPower to cutoff
 input_nGenesNetwork <- 1000    #Number of top genes
 input_minModuleSize <- 20  #Module size minimum 
 wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq  slope truncated.R.sq mean.k. median.k. max.k.
## 1      1   0.1630  0.973          0.880 347.000   349.000  510.0
## 2      2   0.0146 -0.157          0.881 166.000   161.000  320.0
## 3      3   0.3010 -0.679          0.911  92.200    85.200  218.0
## 4      4   0.5360 -1.140          0.877  55.700    48.600  158.0
## 5      5   0.6210 -1.340          0.870  35.800    29.300  118.0
## 6      6   0.6410 -1.600          0.874  24.000    18.400   91.1
## 7      7   0.7000 -1.640          0.903  16.700    12.100   72.0
## 8      8   0.7490 -1.680          0.918  12.000     8.200   58.0
## 9      9   0.7820 -1.700          0.922   8.810     5.740   47.5
## 10    10   0.8130 -1.730          0.920   6.630     4.070   39.4
## 11    12   0.8930 -1.630          0.913   3.960     2.180   28.0
## 12    14   0.8480 -1.660          0.826   2.530     1.270   20.6
## 13    16   0.8340 -1.750          0.794   1.700     0.769   17.0
## 14    18   0.8480 -1.760          0.807   1.210     0.495   14.6
## 15    20   0.3160 -2.840          0.182   0.891     0.324   12.9
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
## Error in cutree(dendro, h = heightcutoff): the 'height' component of 'tree' is not sorted (increasingly)
 softPower()  # soft power curve 
## Error in softPower(): object 'wgcna.out' not found
  modulePlot()  # plot modules  
## Error in modulePlot(): object 'wgcna.out' not found
  listWGCNA.Modules.out = listWGCNA.Modules() #modules
## Error in listWGCNA.Modules(): object 'wgcna.out' not found
 input_selectGO5 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO5,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_selectWGCNA.Module <- '1. turquoise (180 genes)' #Select a module
 input_topGenesNetwork <- 10    #SoftPower to cutoff
 input_edgeThreshold <- 0.4 #Number of top genes 
 moduleNetwork()    # show network of top genes in selected module
## Error in moduleNetwork(): object 'wgcna.out' not found
 input_removeRedudantSets <- TRUE   #Remove redundant gene sets 
 results <- networkModuleGO()  #Enrichment analysis of selected module
## Error in networkModuleGO(): object 'wgcna.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
1.8e-60 216 Response to abiotic stimulus
4.0e-48 189 Response to organic substance
2.1e-46 168 Response to hormone
1.7e-45 168 Response to endogenous stimulus
5.0e-42 158 Response to oxygen-containing compound
2.9e-39 149 Cellular response to chemical stimulus
7.1e-35 133 Response to external stimulus
1.3e-32 160 Cell communication
1.3e-32 106 Response to inorganic substance
2.4e-32 120 Response to acid chemical